Artificial Intelligence-Driven Cross-Language Communication Network: Using Neural Network to Improve Foreign Language Translation

Translating written or spoken words from one language to another while preserving their original intent, meaning, and tone is referred to as language translation and it is both an art and a science. Communication across linguistic borders is made possible by language translation, which is essential for tying people, businesses, and cultures together. Translation issues include mistranslated words and the challenge of translating lengthy sentences. We researched sentence-level machine translation techniques based on deep learning and developed an electronic dictionary for Chinese and English. In this study, we proposed a novel battle royale dynamic recurrent neural network (BR-DRNN) for translating Chinese to English. In this research, we gathered information from the WiLI- dataset. The suggested approach and translation model have been used to train the data. The proposed method is compared to the other existing algorithms. The result shows the BR-DRNN has better performance in terms of accuracy, BLEU score, precision, recall, and F1-score than other algorithms. This study highlights artificial intelligence’s (AI) potential to transform language communication by providing a scalable and effective solution. The translation system powered by a neural network improves communication in a variety of circumstances.